Weekly Exercises #3

library(tidyverse)     # for graphing and data cleaning
library(gardenR)       # for Lisa's garden data
library(lubridate)     # for date manipulation
library(ggthemes)      # for even more plotting themes
library(geofacet)      # for special faceting with US map layout
theme_set(theme_minimal())       # My favorite ggplot() theme :)

# Lisa's garden data
data("garden_harvest")

# Seeds/plants (and other garden supply) costs
data("garden_spending")

# Planting dates and locations
data("garden_planting")

# Tidy Tuesday dog breed data
breed_traits <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-02-01/breed_traits.csv')
trait_description <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-02-01/trait_description.csv')
breed_rank_all <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-02-01/breed_rank.csv')

# Tidy Tuesday data for challenge problem
kids <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-15/kids.csv')

Setting up on GitHub!

Before starting your assignment, you need to get yourself set up on GitHub and make sure GitHub is connected to R Studio. To do that, you should read the instruction (through the “Cloning a repo” section) and watch the video here. Then, do the following (if you get stuck on a step, don’t worry, I will help! You can always get started on the homework and we can figure out the GitHub piece later):

• Create a repository on GitHub, giving it a nice name so you know it is for the 3rd weekly exercise assignment (follow the instructions in the document/video).
  
• Copy the repo name so you can clone it to your computer. In R Studio, go to file –> New project –> Version control –> Git and follow the instructions from the document/video.
  
• Download the code from this document and save it in the repository folder/project on your computer.
  
• In R Studio, you should then see the .Rmd file in the upper right corner in the Git tab (along with the .Rproj file and probably .gitignore).
  
• Check all the boxes of the files in the Git tab and choose commit.
  
• In the commit window, write a commit message, something like “Initial upload” would be appropriate, and commit the files.
  
• Either click the green up arrow in the commit window or close the commit window and click the green up arrow in the Git tab to push your changes to GitHub.
  
• Refresh your GitHub page (online) and make sure the new documents have been pushed out.
  
• Back in R Studio, knit the .Rmd file. When you do that, you should have two (as long as you didn’t make any changes to the .Rmd file, in which case you might have three) files show up in the Git tab - an .html file and an .md file. The .md file is something we haven’t seen before and is here because I included keep_md: TRUE in the YAML heading. The .md file is a markdown (NOT R Markdown) file that is an interim step to creating the html file. They are displayed fairly nicely in GitHub, so we want to keep it and look at it there. Click the boxes next to these two files, commit changes (remember to include a commit message), and push them (green up arrow).
  
• As you work through your homework, save and commit often, push changes occasionally (maybe after you feel finished with an exercise?), and go check to see what the .md file looks like on GitHub.
  
• If you have issues, let me know! This is new to many of you and may not be intuitive at first. But, I promise, you’ll get the hang of it!

Instructions

• Put your name at the top of the document.

• For ALL graphs, you should include appropriate labels.

• Feel free to change the default theme, which I currently have set to theme_minimal().

• Use good coding practice. Read the short sections on good code with pipes and ggplot2. This is part of your grade!

• When you are finished with ALL the exercises, uncomment the options at the top so your document looks nicer. Don’t do it before then, or else you might miss some important warnings and messages.

Warm-up exercises with garden data

These exercises will reiterate what you learned in the “Expanding the data wrangling toolkit” tutorial. If you haven’t gone through the tutorial yet, you should do that first.

1. Summarize the garden_harvest data to find the total harvest weight in pounds for each vegetable and day of week (HINT: use the wday() function from lubridate). Display the results so that the vegetables are rows but the days of the week are columns.

garden_harvest %>% 
  mutate(day = wday(date, label = TRUE)) %>% 
  group_by(vegetable, day) %>% 
  summarize(total_harvest_weight = sum(weight)) %>% 
  mutate(wt_lbs = total_harvest_weight * 0.00220462) %>% 
  pivot_wider(id_cols = vegetable, 
              names_from = day, 
              values_from = wt_lbs)

2. Summarize the garden_harvest data to find the total harvest in pound for each vegetable variety and then try adding the plot from the garden_planting table. This will not turn out perfectly. What is the problem? How might you fix it?

garden_harvest %>% 
  group_by(vegetable, variety) %>% 
  summarize(total_harvest_weight = sum(weight)) %>% 
  mutate(wt_lbs = total_harvest_weight * 0.00220462) %>% 
   left_join(garden_planting, 
            by = "vegetable", "variety")

**The issue that arises when adding the plot from the garden_planting dataset to the garden_harvest dataset is particular variety plotted in two different plots. Variety of vegetable appears twice in joined set.

3. I would like to understand how much money I “saved” by gardening, for each vegetable type. Describe how I could use the garden_harvest and garden_spending datasets, along with data from somewhere like this to answer this question. You can answer this in words, referencing various join functions. You don’t need R code but could provide some if it’s helpful.

To understand how much money you saved by gardening instead of buying produce at the grocery store, the garden_harvest and garden_spending datasets need to be joined. We want to be able to work with the price_with_tax variable and get a count of vegetable by variety. We would multiply price_with_tax by number of vegetable by variety, which gives you a total to compare with the prices listed on the Whole Foods website (or prices you could find in store).

4. Subset the data to tomatoes. Reorder the tomato varieties from smallest to largest first harvest date. Create a barplot of total harvest in pounds for each variety, in the new order.CHALLENGE: add the date near the end of the bar. (This is probably not a super useful graph because it’s difficult to read. This is more an exercise in using some of the functions you just learned.)

garden_harvest %>% 
  filter(vegetable %in% "tomatoes") %>% 
  group_by(variety) %>% 
  mutate(weight_lb = weight * 0.00220462) %>%
  ggplot(aes(x = weight_lb, 
             y = fct_reorder(variety, date, min, .desc = TRUE), 
             color = variety, 
             fill = variety))+
  geom_col() +
  labs(x = "Weight (lbs)", 
       y = "", 
       title = "Total Tomato Harvest for Each Variety") +
  theme_clean()

5. In the garden_harvest data, create two new variables: one that makes the varieties lowercase and another that finds the length of the variety name. Arrange the data by vegetable and length of variety name (smallest to largest), with one row for each vegetable variety. HINT: use str_to_lower(), str_length(), and distinct().

garden_harvest %>% 
  mutate(variety_length = str_length(variety)) %>% 
  mutate(variety_lower = str_to_lower(variety)) %>% 
  arrange(vegetable, variety_length) %>% 
  group_by(variety) %>% 
  filter(row_number() ==1)

6. In the garden_harvest data, find all distinct vegetable varieties that have “er” or “ar” in their name. HINT: str_detect() with an “or” statement (use the | for “or”) and distinct().

garden_harvest %>% 
  mutate(has_er = str_detect(variety, "er")) %>% 
  mutate(has_ar = str_detect(variety, "ar"))

Bicycle-Use Patterns

In this activity, you’ll examine some factors that may influence the use of bicycles in a bike-renting program. The data come from Washington, DC and cover the last quarter of 2014.

A typical Capital Bikeshare station. This one is at Florida and California, next to Pleasant Pops.

One of the vans used to redistribute bicycles to different stations.

Two data tables are available:

• Trips contains records of individual rentals
• Stations gives the locations of the bike rental stations

Here is the code to read in the data. We do this a little differently than usual, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}.

data_site <- 
  "https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data.rds" 
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")

NOTE: The Trips data table is a random subset of 10,000 trips from the full quarterly data. Start with this small data table to develop your analysis commands. When you have this working well, you should access the full data set of more than 600,000 events by removing -Small from the name of the data_site.

Temporal patterns

It’s natural to expect that bikes are rented more at some times of day, some days of the week, some months of the year than others. The variable sdate gives the time (including the date) that the rental started. Make the following plots and interpret them:

7. A density plot, which is a smoothed out histogram, of the events versus sdate. Use geom_density().

Trips %>% 
  select(sdate) %>% 
  ggplot(aes(x = sdate)) +
  geom_density() +
  labs(x = "Date", 
       y = "Density", 
       title = "Bike Rentals By Date") +
  theme_economist()

8. A density plot of the events versus time of day. You can use mutate() with lubridate’s hour() and minute() functions to extract the hour of the day and minute within the hour from sdate. Hint: A minute is 1/60 of an hour, so create a variable where 3:30 is 3.5 and 3:45 is 3.75.

Trips %>% 
  select(sdate) %>% 
  mutate(hr = hour(sdate)) %>% 
  mutate(min = minute(sdate)) %>% 
  mutate(time_of_day = hr + (min/60)) %>% 
  ggplot(aes(x = time_of_day)) +
  geom_density() +
  labs(x = "Time of Day", 
       y = "Density", 
       title = "Bike Rentals By Time of Day") +
  theme_classic()

9. A bar graph of the events versus day of the week. Put day on the y-axis.

Trips %>% 
  select(sdate) %>% 
  mutate(day_of_week = wday(sdate, label = TRUE)) %>% 
  ggplot(aes(y = day_of_week, 
             color = day_of_week, 
             fill = day_of_week)) +
  geom_bar() +
  labs(x = "Trips", 
       y = "")

10. Facet your graph from exercise 8. by day of the week. Is there a pattern?

Trips %>% 
  select(sdate) %>% 
  mutate(day_of_week = wday(sdate, label = TRUE)) %>% 
  mutate(hr = hour(sdate)) %>% 
  mutate(min = minute(sdate)) %>% 
  mutate(time_of_day = hr + (min/60)) %>% 
  ggplot(aes(x = time_of_day)) +
  geom_density() +
  facet_wrap(~ day_of_week) +
  labs(x = "Time of Day", 
       y = "Density", 
       title = "Bike Rentals By Time of Day") +
  theme_classic()

There is a clear pattern of differentiation between weekdays and weekends. Monday through Friday, there appears to be an uptick in bike usage around the start of the workday (maybe around 9am?) and the end of the workday (maybe around 5pm assuming traditional work hours). On weekends, we do not see this trend as people are obviously not commuting to and from work on those days. We can also assume that there are more casual riders on weekends, maybe even tourists visiting the city.

The variable client describes whether the renter is a regular user (level Registered) or has not joined the bike-rental organization (Causal). The next set of exercises investigate whether these two different categories of users show different rental behavior and how client interacts with the patterns you found in the previous exercises.

11. Change the graph from exercise 10 to set the fill aesthetic for geom_density() to the client variable. You should also set alpha = .5 for transparency and color=NA to suppress the outline of the density function.

Trips %>% 
  mutate(day_of_week = wday(sdate, label = TRUE)) %>% 
  mutate(hr = hour(sdate)) %>% 
  mutate(min = minute(sdate)) %>% 
  mutate(time_of_day = hr + (min/60)) %>% 
  ggplot(aes(x = time_of_day, 
             fill = client)) +
  geom_density(alpha = .5,
               color=NA) +
  facet_wrap(~ day_of_week) +
  labs(x = "Time of Day", 
       y = "Density", 
       title = "Bike Rentals By Time of Day") +
  theme_classic()

12. Change the previous graph by adding the argument position = position_stack() to geom_density(). In your opinion, is this better or worse in terms of telling a story? What are the advantages/disadvantages of each?

Trips %>% 
  mutate(day_of_week = wday(sdate, label = TRUE)) %>% 
  mutate(hr = hour(sdate)) %>% 
  mutate(min = minute(sdate)) %>% 
  mutate(time_of_day = hr + (min/60)) %>% 
  ggplot(aes(x = time_of_day, 
             fill = client)) +
  geom_density(position = position_stack(), 
               alpha = .5, 
               color=NA) +
  facet_wrap(~ day_of_week) +
  labs(x = "Time of Day", 
       y = "Density", 
       title = "Bike Rentals By Time of Day") +
  theme_classic()

In my opinion, the non-position stacked graph is clearer and better in terms of telling a story. In the stacked graph, it is very difficult to differentiate where casual starts and registered ends. With the non-position stacked graph, we can still clearly visualize the trends of the peaks and ebbs in bike usage, but this allows us to better visualize casual and registered clients as two separate entities. The advantage to the stacked graph, however, is that this is a better visualize of the two client groups as part of a whole.

13. In this graph, go back to using the regular density plot (without position = position_stack()). Add a new variable to the dataset called weekend which will be “weekend” if the day is Saturday or Sunday and “weekday” otherwise (HINT: use the ifelse() function and the wday() function from lubridate). Then, update the graph from the previous problem by faceting on the new weekend variable.

Trips %>% 
  mutate(day_of_week = wday(sdate, label = TRUE)) %>%
  mutate(weekend = ifelse(day_of_week == c("Sat", "Sun"), "weekend", "weekday")) %>% 
  mutate(hr = hour(sdate)) %>% 
  mutate(min = minute(sdate)) %>% 
  mutate(time_of_day = hr + (min/60)) %>% 
  ggplot(aes(x = time_of_day, 
             fill = client, 
             alpha = .5)) +
  geom_density(color = NA, 
               alpha = 0.5) +
  facet_wrap(~ weekend) +
  labs(x = "Time of Day (Hrs)", 
       y = "Event Density", 
       title = "Bike Rentals By Time of Day") +
  theme_classic()

14. Change the graph from the previous problem to facet on client and fill with weekend. What information does this graph tell you that the previous didn’t? Is one graph better than the other?

Trips %>% 
  mutate(day_of_week = wday(sdate, label = TRUE)) %>%
  mutate(weekend = ifelse(day_of_week == c("Sat", "Sun"), "weekend", "weekday")) %>% 
  mutate(hr = hour(sdate)) %>% 
  mutate(min = minute(sdate)) %>% 
  mutate(time_of_day = hr + (min/60)) %>% 
  ggplot(aes(x = time_of_day, 
             fill = weekend)) +
  geom_density(alpha = .5, 
               color=NA) +
  facet_wrap(~ client) +
  labs(x = "Time of Day", 
       y = "Density", 
       title = "Bike Rentals By Time of Day") +
  theme_classic()

This graph better allows us to see trends in temporal usage based on client type. From this graph, we can see that there is clear differentiation between casual and registered users in terms of what part of the day and what days they are using the bikes. We can even perhaps generalize that registered users are more likely to be using the bikes to commute to and from work, whereas the casual users are perhaps tourists or using the bikes for leisure, or not adhering to a traditional work schedule.

Spatial patterns

15. Use the latitude and longitude variables in Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. We will improve this plot next week when we learn about maps!

Trips %>% 
  left_join(Stations, 
            by=c("sstation"="name")) %>% 
  group_by(lat, long) %>% 
  summarize(n = n(), 
            prop_casual = mean(client == "Casual")) %>% 
  ggplot(aes(x = long, 
             y = lat, 
             color = n)) +
  geom_point(shape = 17) +
  labs(x = "Longitude", 
       y = "Latitude", 
       title = "Total Number of Departures From Each Station")

16. Only 14.4% of the trips in our data are carried out by casual users. Create a plot that shows which area(s) have stations with a much higher percentage of departures by casual users. What patterns do you notice? (Again, we’ll improve this next week when we learn about maps).

Trips %>% 
  left_join(Stations, 
            by=c("sstation"="name")) %>% 
  group_by(lat, long) %>% 
  summarize(n = n(), 
            prop_casual = mean(client == "Casual")) %>%
  ggplot(aes(x = long, 
             y = lat, 
             color = prop_casual)) +
  geom_point(shape = 17) +
  labs(x = "Longitude", 
       y = "Latitude", 
       title = "Departures from Stations By Casual Users")

We can see that there is a high concentration of usage by casual riders clustered around the center of the points, which perhaps correponds with either popular tourist attractions in DC, or the city center. The riders using the bikes at these locations are more likely to be riding for leisure than registered riders, who, as we have established, are more likely to be using the bikes to commute to and from work.

DID YOU REMEMBER TO GO BACK AND CHANGE THIS SET OF EXERCISES TO THE LARGER DATASET? IF NOT, DO THAT NOW.

Dogs!

In this section, we’ll use the data from 2022-02-01 Tidy Tuesday. If you didn’t use that data or need a little refresher on it, see the website.

17. The final product of this exercise will be a graph that has breed on the y-axis and the sum of the numeric ratings in the breed_traits dataset on the x-axis, with a dot for each rating. First, create a new dataset called breed_traits_total that has two variables – Breed and total_rating. The total_rating variable is the sum of the numeric ratings in the breed_traits dataset (we’ll use this dataset again in the next problem). Then, create the graph just described. Omit Breeds with a total_rating of 0 and order the Breeds from highest to lowest ranked. You may want to adjust the fig.height and fig.width arguments inside the code chunk options (eg. {r, fig.height=8, fig.width=4}) so you can see things more clearly - check this after you knit the file to assure it looks like what you expected.

breed_traits_total <- breed_traits %>% 
  mutate(Breed_Squish = str_squish(Breed)) %>%
  select(-c(`Coat Type`, `Coat Length`)) %>% 
  pivot_longer(cols = c(`Affectionate With Family`:`Drooling Level`,`Openness To Strangers`:`Mental Stimulation Needs`), 
               names_to = "traits", 
               values_to = "rating") %>%
  group_by(Breed_Squish) %>% 
  summarize(total_rating = sum(rating)) %>% 
  filter(total_rating > 0) 

breed_traits_total %>% 
  ggplot(aes(x = total_rating, 
             y = fct_reorder(Breed_Squish, total_rating))) +
  geom_point() +
  labs(x = "Total Cumulative Rating", 
       y = "", 
       title = "Total Rating By Dog Breed")

breed_traits_total

18. The final product of this exercise will be a graph with the top-20 dogs in total ratings (from previous problem) on the y-axis, year on the x-axis, and points colored by each breed’s ranking for that year (from the breed_rank_all dataset). The points within each breed will be connected by a line, and the breeds should be arranged from the highest median rank to lowest median rank (“highest” is actually the smallest numer, eg. 1 = best). After you’re finished, think of AT LEAST one thing you could you do to make this graph better. HINTS: 1. Start with the breed_rank_all dataset and pivot it so year is a variable. 2. Use the separate() function to get year alone, and there’s an extra argument in that function that can make it numeric. 3. For both datasets used, you’ll need to str_squish() Breed before joining.

breed_rank_all %>%
  mutate(Breed_Squish = str_squish(Breed)) %>% 
  left_join(breed_traits_total,
              by = c("Breed_Squish")) %>% 
  arrange(desc(total_rating)) %>% 
  slice(1:20) %>% 
  pivot_longer(cols = starts_with("20"), 
               names_to = "Year", 
               values_to = "Ranking") %>%
  separate(Year, 
           into = c("Str_Year", "string"), 
           remove = FALSE) %>% 
  mutate(Year = as.numeric(Str_Year)) %>% 
  select(-string, -Str_Year) %>% 
  group_by(Year, Breed_Squish, Ranking) %>%
  mutate(median_ranking = median(Ranking)) %>% 
  ggplot(aes(x = Year, 
             y = fct_reorder(Breed_Squish, median_ranking, .desc = TRUE), 
             color = Ranking)) +
  geom_point() +
  geom_line() +
  scale_color_viridis_c() +
  labs(x = "Year", 
       y = "", 
       title = "Dog Breed Median Ranking From 2013 - 2020")

One thing that I immediately notice about this graph is the fact that Miniature American Shepherds did not rank as anything until 2020. Because of the NAs in the dataset, this threw off the visualization a little bit and left us with gray points and lines from 2013-2019, which is slightly confusing visually. I could have removed the NAs from the dataset for a clearer data visualization.

19. Create your own! Requirements: use a join or pivot function (or both, if you’d like), a str_XXX() function, and a fct_XXX() function to create a graph using any of the dog datasets. One suggestion is to try to improve the graph you created for the Tidy Tuesday assignment. If you want an extra challenge, find a way to use the dog images in the breed_rank_all file - check out the ggimage library and this resource for putting images as labels.

breed_rank_all %>%
  mutate(Breed_Squish = str_squish(Breed)) %>% 
  left_join(breed_traits_total,
              by = c("Breed_Squish")) %>%
  pivot_longer(cols = starts_with("20"), 
               names_to = "Year", 
               values_to = "Ranking") %>%
    separate(Year, 
           into = c("Str_Year", "string"), 
           remove = FALSE) %>%
  mutate(Year = as.numeric(Str_Year)) %>% 
  select(-string, -Str_Year) %>% 
  select(Breed_Squish, total_rating, Year, Ranking) %>% 
  filter(Breed_Squish %in% c("Retrievers (Labrador)", "Australian Shepherds", "Border Collies", "Australian Cattle Dogs")) %>%
  arrange(desc(total_rating)) %>%
  mutate(median_ranking = median(Ranking)) %>%  
  group_by(Breed_Squish) %>%
  ggplot(aes(x = Year, 
             y = fct_reorder(Breed_Squish, median_ranking, .desc = TRUE), 
             color = Ranking)) +
  geom_point() +
  geom_line() +
    scale_color_continuous() +
  labs(x = "Year", 
       y = "", 
       title = "Median Ranking of Cattle Working Dogs from 2013-2020") +
  theme_clean()

GitHub link

20. Below, provide a link to your GitHub page with this set of Weekly Exercises. Specifically, if the name of the file is 03_exercises.Rmd, provide a link to the 03_exercises.md file, which is the one that will be most readable on GitHub.

https://github.com/mackenzieclarke/03_Exercises

Challenge problem!

This problem uses the data from the Tidy Tuesday competition this week, kids. If you need to refresh your memory on the data, read about it here.

21. In this exercise, you are going to try to replicate the graph below, created by Georgios Karamanis. I’m sure you can find the exact code on GitHub somewhere, but DON’T DO THAT! You will only be graded for putting an effort into this problem. So, give it a try and see how far you can get without doing too much googling. HINT: use facet_geo(). The graphic won’t load below since it came from a location on my computer. So, you’ll have to reference the original html on the moodle page to see it.

DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?